Does facial width-to-height ratio predict male offender aggression?
Bibliographic record
Abstract
Purpose Based on the previously observed link between greater facial width-to-height ratio (fWHR) and interpersonal aggression in men (see Haselhuhn et al., 2015), the purpose of this paper is to test whether fWHR could differentiate among male offenders as a function of the relative aggressiveness of the crime for which they had been convicted. Design/methodology/approach fWHR measurements (n=550) were computed based on a large subset of male offenders available on a public domain database. Each offender’s index offense and possible confounding variables such as age, ethnicity, and body mass index were also recorded. Findings Multiple analyses yielded no evidence of a relationship between male fWHR and the comparative level of violence of their conviction offense. Originality/value Establishing an empirical basis for probable parameters of an unknown offender’s facial structure could have a considerable practical value for criminal profiling purposes. fWHR – at least as it has been most frequently assessed – does not appear to be a facial parameter that is useful for this purpose, however.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".